This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt, and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas—the Santana district in São Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands—were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

Building information modelling (BIM) and geoinformation are widely recognised as complementary sources of data. Whereas a BIM model can represent a single building or infrastructure project in high detail, geoinformation-based sources can represent different types of features in a large region with less detail. Integrating geoinformation and BIM is very useful in practice and constitutes an active research field—often referred to as GeoBIM. A short list of GeoBIM applications include: performing checks for the issuance of building permits using buildings (BIM) and city regulations (Geo), navigation that combines outdoor (Geo) and indoor (BIM) portions, facility management for infrastructure sites (BIM) that include the regional connections between the sites (Geo), and risk management using regional simulations (Geo) that also takes into account the impact on specific sites (BIM). [...]

Urban morphological analytics on buildings is informative for sustainable development. 3D building massing features, such as courtyards and setbacks, reflect spatial organizations and circulations, while influence daylight access, ventilation, and shading. However, existing 3D GIS methods usually overlook such 3D massing features, further obscure morphological analytics and environmental assessment. This article proposes MorphCut, an efficient convex decomposition method that segments 3D shapes into mass-aligned parts. MorphCut leverages key morphological properties—planarity, regularity, and Gestalt laws—after a topological preprocessing step to enable mass-aware decomposition. Experiments on representative samples, ranging from small houses to complex skyscrapers, showed that MorphCut outperformed four baseline methods in (i) balancing convexity and compactness, (ii) aligning decomposed parts with building masses, and (iii) preserving geometric fidelity (average deviation: 0.25 m). An urban-scale validation on datasets from Delft and Hong Kong, comprising over 30,000 buildings across 18.3 km², demonstrated MorphCut’s robustness, scalability, and generalizability. MorphCut successfully decomposed 98% of buildings in low-rise regions (+78% over the second-best method) and 93% in high-rise areas (+2%), completing processing in 13 hours (3 hours faster). These results position MorphCut as a foundational 3D GIS tool for large-scale, mass-aware morphological analysis, with implications for digital twins, sustainable planning, and environmental modeling.

Data-driven prediction of infrastructure aging is challenging due to the complex stochastic nature of degradation effects and the ill-documented historical records. Degradation modeling is, however, crucial for predictive maintenance that is key for infrastructure integrity. This study presents a multi-attribute, data-driven approach for modelling stochastic degradation and maintenance effects of roads, mining an extensive database of geo-located historical inspection and maintenance records from the municipality of Amsterdam. Inspection data track pavement conditions at irregular intervals across ten discrete states per road segment, following the Dutch CROW 146 protocol. Damage severity and extent for eight damage modes is captured, i.e., for transverse unevenness, irregularities, ravelling, edge damage, crack formation, joint filling, joint width, and settling. The maintenance dataset includes >25k minor interventions across 17k road segments, indicating repair requirements, and 200+ major maintenance projects, covering 21k segments where interventions are planned, all without verifying completion. This complicates accurate modelling of natural degradation as it is confounded by maintenance effects. To address the issue of irregular inspections, degradation is first modelled as a continuous-time Markov chain. Thereby, transition rates are estimated, which are then converted to discrete-time Markov chain transition probability matrices to eventually support regular maintenance planning. We further learn the effects of minor and major maintenance activities, as defined and recorded in the database. Based on the estimated degradation transitions, pre-maintenance and post-maintenance state distributions are estimated. Instantaneous maintenance transition matrices are computed by minimizing the cross-entropy between the pre-maintenance state after the intervention and the post-maintenance state. The model allows for a multi-attribute approach, segmenting roads based on construction material (e.g., asphalt, tiled pavement) and traffic loads (e.g., residential, commercial/pedestrian). The approach is exemplified for tiled pavements for a section of the road network of Amsterdam, where the effects of minor and major maintenance are ablated for long-term predictions. Although applied to Amsterdam, this method is relevant to any infrastructure system with discrete state datasets, providing a foundation for data-driven decision-making in infrastructure management.

Accurate lane maps with semantics are crucial for various applications, such as high-definition maps (HD Maps), intelligent transportation systems (ITS), and digital twins. Manual annotation of lanes is labor-intensive and costly, prompting researchers to explore automatic lane extraction methods. This paper presents an end-to-end large-scale lane mapping method that considers both lane geometry and semantics. This study represents lane markings as polylines with uniformly sampled points and associated semantics, allowing for adaptation to varying lane shapes. Additionally, we propose an end-to-end network to extract lane polylines from mobile laser scanning (MLS) data, enabling the inference of vectorized lane instances without complex post-processing. The network consists of three components: a feature encoder, a column proposal generator, and a lane information decoder. The feature encoder encodes textual and structural information of lane markings to enhance the method’s robustness to data imperfections, such as varying lane intensity, uneven point density, and occlusion-induced incomplete data. The column proposal generator generates regions of interest for the subsequent decoder. Leveraging the embedded multi-scale features from the feature encoder, the lane decoder effectively predicts lane polylines and their associated semantics without requiring step-by-step conditional inference. Comprehensive experiments conducted on three lane datasets have demonstrated the performance of the proposed method, even in the presence of incomplete data and complex lane topology. Furthermore, the datasets used in this work, including source ground points, generated bird’s eye view (BEV) images, and annotations, will be publicly available with the publication of the paper.

The role and adoption of 3D city models have been changing from a data endpoint to a centralised data source that is used for a variety of different analyses in different sectors. This change has not yet been fully completed and the transition process is still very noticeable at certain places. For example, data required for city-scale analyses are often missing, incorrect, or not stored in a standard way. A subset of these data (E.g. shell volume, shell area & footprint area) can be approximated from lower LoD shapes (LoD2.2 or lower) in the 3D city models. However, these models frequently simplify reality and therefore these approximations are not accurate. This paper proposes computing these data by voxelising Building Information Modelling (BIM) models representing the same buildings as the 3D city model. It is shown that a subset of these approximations (shell volume & footprint area) are more accurate than values computed from lower LoD shapes. Storing these data as attributes of the building models in 3D city models can improve the ease of use and the outcome of city-scale analyses. The computed values from BIM models can also be assigned to outputs of BIM to Geo conversions. This overturns the accuracy loss of the geometry caused by the conversion in which geometry is significantly generalised and simplified.

Historic gardens, regarded as a significant genre of cultural heritage, encapsulate the enduring essence of bygone eras while concurrently transcending temporal boundaries to resonate with the present and future. These gardens provide us vitality and inspiration, holding a collective repository of human memory and serving as a testament to our shared heritage. However, like landscapes, gardens constantly change through natural processes and human interventions. How can we preserve these gardens, though changes are unavoidable? Spatial and visual characteristics are the gardens' essential characteristics, and point-cloud (LiDAR) technologies are powerful tools to reveal and analyze gardens’ spatial-visual relationships and characteristics. Therefore, this paper aims to present a point-cloud-based approach to identifying spatial-visual design principles and making them operational to protect and develop historic gardens. Additionally, several methods have been proposed in this research, including (a) a voxel-based method to transfer points into a solid model for GIS-based computation, (b) a novel method to analyze the field of view (FOV), and (c) a systemic framework to reveal historic gardens’ spatial-visual characteristics based on the voxelized model. Jichang Garden, a historic garden in Wuxi, China, known for its visual design and spatial arrangement, has been selected as a case study to showcase how to apply the methods proposed by this paper. The findings include the design principles for the water body, the arrangement for a route, and the planting strategies of the garden. The conservational strategies have been formed based on the findings, and the appliable potentials and limitations of the methods have also been discussed.

The integration of geoinformation with Building Information Models (BIM), termed GeoBIM, has garnered significant attention across academic and non-academic sectors due to its potential for analyzing the reciprocal impacts of new designs on their environment. However, achieving integration between 3D city models and BIM necessitates ensuring consistency and alignment between their respective features and specifications. Georeferencing, a fundamental task in GeoBIM, involves establishing a connection between digital models and the Earth’s surface through coordinate transformations. Despite its importance, accurate georeferencing of BIM models has often been overlooked, resulting in challenges for integrating BIM models and geographical data. To address this gap, our study proposes a novel approach to enhance the georeferencing accuracy of BIM models by integrating surveyed points, considering the varying levels of georeferencing precision applicable to Industry Foundation Classes (IFC) models. We explore the potential benefits and challenges associated with this integrated surveyed point methodology, providing insights to improve georeferencing within the GeoBIM framework.

Delivering value from digital concepts such as Digital Twins is necessary to address systemic national and global issues, such as achieving Net Zero. However, there is still a lack of consensus over what a Digital Twin (DT) is and efforts to clarify this do not consider the Geospatial perspective. With the aspiration for national- and international-scale DTs, it is important that the Geospatial community understands its role in supporting the realisation of the value of these DTs. Here, a systematic literature review is used to gather DT case studies that use, or are inferred to use, elements of the Geospatial discipline. A total of 77 DT case studies about smart cities, manufacturing, energy, construction and agriculture are reviewed in full, and 24 Geospatial DT dimensions are defined and then compared with existing DT dimensions. The results indicate a considerable use of Geospatial Science in DTs that is not explicitly stated, meaning that there are possibly missed opportunities for collaboration between the Geospatial and DT communities. We conclude that the role of Geospatial Science in DTs is larger than stated and needs to be understood further.

The potential of Building Information Models (BIM) to establish rights and responsibilities for multi-level building complexes in cadastral registrations has been explored in many previous researches. However, the implementation of BIM-based cadastral registrations in practice remains limited due to the complex interplay between technical potentials and legal implications as well as uncertainty about the additional complexity of a BIM model and the extra work that will be required to generate the BIM Legal model. In collaboration with Netherlands Kadaster, we have investigated the data requirements for a BIM Legal model that will support the 3D cadastral registration of apartment complexes and that aligns with BIM creation processes in practice. The BIM Legal model aims to visualise the spatial aspects of apartment complexes and to align the cadastral registration to the 3D data that is generated in the design and construction phase of new buildings. The BIM Legal file will be submitted when an apartment complex is being registered and is based on IFC, the open standard established by BuildingSMART International and used in the BIM domain. To make the implementation of BIM Legal feasible legally, technically, and economically, we apply a 3-phase approach. This paper presents the BIM Legal model as defined in Phase 1, which enables to generate a BIM Legal model compliant with current legislation frameworks with no/minimal manual interaction from BIM models as commonly generated in design processes. The paper describes the 3-phase approach, the context of Phase 1, the data requirement analyses, the defined BIM Legal data model, as well the questions that emerged during the specification process that need to be answered to further improve the implementation of Phase 1 and to further develop BIM Legal for Phases 2 and 3.

3D modeling of indoor spaces is a prerequisite for daylight simulation, and the accuracy of the 3D models has a significant impact on the simulation. The goal of this study was to quantify the errors caused by modeling indoor spaces at different accuracy levels to find the optimal balance between the reliability of the results and labor investment. For this purpose, we introduce a level of detail (LOD) concept for indoor spaces based on the size of non-permanent indoor objects by inclusion and exclusion from the simulation scene. The errors corresponding to models with low accuracies are measured by climate-based simulation using an improved two-phase method. Our results show that inaccurate modeling of indoor spaces causes between 10-70% error in TAI with 25% median across all spaces.

Although level of detail (LoD) is a central concept in 3D city modelling, specifying different LoDs in an unambiguous manner is not straightforward. To resolve this, a set of frameworks have been developed. This paper evaluates the suitability of the LoD framework of (Biljecki et al. 2016) for 3D building models that have been generated directly from BIM models. The output of two BIM shell extractors are tested on how well they can be defined by the framework. It was found that although BIM-derived models can be specified by the framework to a certain degree, the framework is not fully capable to also specify lower quality models and to support all the output that may come from BIM shell extractors. This can be resolved by either addressing issues in the shell extractors’ output or in the framework itself. The results of this research can be used to improve the LoD framework and to adjust the shell extractors output to better comply with unambiguous definitions of building models at different LoDs and could be a first step to standardise the conversion of BIM models at different LoDs to be used in urban applications.

This paper suggests a method to simulate interactions between buildings and their outdoor conditions at the city-scale using a coupled scheme whose physical parameters are entirely assessed from data of the indoor and outdoor built environment. The coupled scheme consists of a reduced order building energy model and a single layer urban canopy model. In a previous study, it was proven that physical parameters of a single layer urban canopy model can be assessed using measurements of the outdoor temperature and humidity in a street canyon. For the coupled scheme to be fully data driven, the next step is to demonstrate that the reduced order building energy model can estimate the cooling consumption and exterior wall surface temperature in good agreement with measurements or simulated data after being trained using machine learning. Indeed, results show that a multi objective genetic algorithm can find values for physical parameters of the reduced order building energy model. Estimates of the cooling consumption and exterior wall surface temperature provided by the trained model achieve a CV-RMSE below 10% and a RMSE lower than 2.5 Kelvin, respectively, with respect to data generated from EnergyPlus. The last step towards a full data driven coupled scheme for city-scale simulations would be to iteratively train the reduce order building energy model with the single layer urban canopy model and show the convergence and accuracy of their respective outputs.

Solar energy is becoming increasingly important with the transition towards green and sustainable energy. Predicting solar irradiance is one of the core steps to optimise solar energy utilisation when planning and scheduling power grids. Accurate solar irradiance prediction can also help forecast microclimate conditions, enabling the analysis of citizens and planning of optimal intervention strategies for heating or cooling behaviour. This paper discusses a novel approach to computing the solar potential of buildings at the city level with promising scalability using semantic 3D city models. Experiments are conducted at different locations in the Netherlands. We evaluate our results by comparing them to the statistical Dutch data, and CitySim shows huge discrepancies in summer.

Optimizing the built environment via simulations of building models hinges on standardizing data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analyzed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties showed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% to 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help to guide a more efficient data acquisition process of indoor spaces for daylight simulations.

The integration of 3D city models and Building Information Models (BIM) in the context of GeoBIM has gained significant attention from both academia and industry. Harmonizing the distinct characteristics and goals of these models is crucial for successful integration. In this paper, we present the development of a plugin for Autodesk Revit, a popular BIM platform, which allows for the incorporation of 3D Geo-data encoded in CityJSON. The plugin, published as open source, enables the generation of individual geometries with associated city model attributes as parameters, facilitating analysing the impact of new or changed buildings (modelled in BIM) on the environment (captured in geo-data). Challenges addressed during development include georeferencing, data format import, handling different geometry approaches, hierarchy of attributes, code optimization, user-friendliness, and enhanced visualization. The plugin contributes to the seamless integration of geo- and BIM data, enhancing interoperability and supporting informed decision-making in the Architecture, Engineering, and Construction and urban domains.

Urban building energy modelling aims at studying different approaches to estimate the energy consumed by buildings. However, most urban building energy models introduced in the literature ignore the impact that buildings have on their outdoor conditions, and inversely. Therefore, the paper determines whether interactions between buildings and outdoor conditions affects simulations performed using an urban building energy model, in particular during their calibration. Results suggest that interactions between buildings and their outdoor conditions affect the sensitivity of outcomes provided by an urban building energy model towards uncertain parameters and the probability to calibrate the model in accordance to some standards.

This paper introduces Property Valuation Application Domain Extension (ADE) within CityGML 3.0, aiming to integrate relevant indoor and outdoor 3D variables (cost estimation, view quality, etc.) for accurate property valuation. Current models lack the necessary features for this specific application. Leveraging IFC data for indoor elements, this ADE extends CityGML, addressing the existing gap. This paper identifies and categorizes data requirements, leading to the conceptualization and development of the model. By enriching CityGML 3.0 with IFC data, the approach introduces new features like the "Property Unit" to ensure adaptability across diverse valuation scenarios. Despite encountering data integrability challenges, we here commit to refining the model and overcoming these obstacles. A preliminary implementation using CityJSON demonstrates successful integration and paves the way for future implementation. These include developing an API platform and establishing an official repository to facilitate practical usability and scalability. This research significantly contributes to advancing property valuation processes by providing accurate valuations for stakeholders and promoting the use of 3D urban data in domain-specific extensions.

This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometric structure or impose stringent input requirements, our approach preserves the integrity of the original model while effectively managing a range of topological errors. The algorithm operates in three distinct phases: (1) pre-processing, which addresses topological errors and identifies pseudo-holes; (2) detecting and extracting complete border rings of holes; and (3) remeshing, aimed at reconstructing the complete geometric surface. Our method demonstrates superior performance compared to related work in filling holes in building mesh models, achieving both uniform local geometry around the holes and structural completeness. Comparative experiments with established methods demonstrate our algorithm’s effectiveness in delivering more complete and geometrically consistent hole-filling results, albeit with a slight trade-off in efficiency. The paper also identifies challenges in handling certain complex scenarios and outlines future directions for research, including the pursuit of a comprehensive repair goal for LoD2 models to achieve watertight 2-manifold models with correctly oriented normals. Our source code is available at https://github.com/tudelft3d/Automatic-Repair-of-LoD2-Building-Models.git

This paper discusses the reconstruction of LoD2 building models from 2D and 3D data for large-scale urban environments. Traditional methods involve the use of LiDAR point clouds, but due to high costs and long intervals associated with acquiring such data for rapidly developing areas, researchers have started exploring the use of point clouds generated from (oblique) aerial images. However, using such point clouds for traditional plane detection-based methods can result in significant errors and introduce noise into the reconstructed building models. To address this, this paper presents a method for extracting rooflines from true orthophotos using line detection for the reconstruction of building models at the LoD2 level. The approach is able to extract relatively complete rooflines without the need for pre-labeled training data or pre-trained models. These lines can directly be used in the LoD2 building model reconstruction process. The method is superior to existing plane detection-based methods and state-of-the-art deep learning methods in terms of the accuracy and completeness of the reconstructed building. Our source code is available at https://github.com/tudelft3d/Roofline-extraction-from-orthophotos.